diff --git a/hacking_religion/chapter_3.qmd b/hacking_religion/chapter_3.qmd index 9426534..0e090e2 100644 --- a/hacking_religion/chapter_3.qmd +++ b/hacking_religion/chapter_3.qmd @@ -27,6 +27,7 @@ library(here) |> suppressPackageStartupMessages() library(tidyverse) # better video device, more accurate and faster rendering, esp. on macos. Also should enable system fonts for display library(ragg) |> suppressPackageStartupMessages() +library(tmap) |> suppressPackageStartupMessages() setwd("/Users/kidwellj/gits/hacking_religion_textbook/hacking_religion") here::i_am("chapter_3.qmd") @@ -36,14 +37,14 @@ if (file.exists(here("data", "infuse_uk_2011_clipped.shp")) == FALSE) { download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_uk_2011_clipped.zip", destfile = "data/infuse_uk_2011_clipped.zip") unzip("data/infuse_uk_2011_clipped.zip", exdir = "data") } -uk_countries <- st_read(here("data", "infuse_uk_2011_clipped.shp"), quiet = TRUE) +uk <- st_read(here("data", "infuse_uk_2011_clipped.shp"), quiet = TRUE) # Download administrative boundaries for whole UK at regions level -if (file.exists(here("data", "infuse_rgn_2011_clipped.shp")) == FALSE) { -download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_rgn_2011_clipped.zip", destfile = "data/infuse_rgn_2011_clipped.zip") -unzip("data/infuse_rgn_2011_clipped.zip", exdir = "data") +if (file.exists(here("data", "infuse_ctry_2011_clipped.zip")) == FALSE) { +download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/infuse_ctry_2011_clipped.zip", destfile = "data/infuse_ctry_2011_clipped.zip") +unzip("data/infuse_ctry_2011_clipped.zip", exdir = "data") } -uk_rgn <- st_read(here("data", "infuse_rgn_2011_clipped.shp"), quiet = TRUE) +uk_countries <- st_read(here("data", "infuse_ctry_2011_clipped.shp"), quiet = TRUE) # Download administrative boundaries for whole UK at local authority level if (file.exists(here("data", "infuse_dist_lyr_2011_clipped.shp")) == FALSE) { @@ -60,7 +61,7 @@ local_authorities_buildings_clip <- st_read(here("data", "infuse_dist_lyr_2011_s Before we move on, let's plot a simple map and have a look at one of our administrative layers. We can use ggplot with a new type of shape `geom_sf()` to plot the contents of a geospatial data file with polygons which is loaded as a `simplefeature` in R. ```{r} -ggplot(uk_countries) + geom_sf() +ggplot(uk) + geom_sf() ``` ## Load in Ordnance Survey OpenMap Points Data @@ -73,20 +74,18 @@ ggplot(uk_countries) + geom_sf() # obtained, see the companion cookbook here: https://github.com/kidwellj/hacking_religion_cookbook/blob/main/ordnance_survey.R os_openmap_pow <- st_read(here("example_data", "os_openmap_pow.gpkg"), quiet = TRUE) - ggplot(os_openmap_pow) + geom_sf() ``` It's worth noting that the way that you load geospatial data in R has changed quite dramatically since 2020 with the introduction of the simplefeature class in R. Much of the documentation you will come across "out there" will make reference to a set of functions which are no longer used, and are worth avoiding. -We could go a bit further with ggplot(), but for this chapter, we're going to primarily use a tool called tmap(), which works a lot like gpplot, but is much better adapted for geospatial data. As you'll see, tmap() also works by adding layers of data and visual instructions one at a time. So we might begin with `tm_shape(uk_countries)` instead of `ggplot(uk_countries) + geom_sf()`. Whereas ggplot() asks us to define the raw data and the shapes to use, tmap() makes some assumptions about the shapes. +We could go a bit further with ggplot(), but for this chapter, we're going to primarily use a tool called tmap(), which works a lot like gpplot, but is much better adapted for geospatial data. As you'll see, tmap() also works by adding layers of data and visual instructions one at a time. So we might begin with `tm_shape(uk)` instead of `ggplot(uk) + geom_sf()`. Whereas ggplot() asks us to define the raw data and the shapes to use, tmap() makes some assumptions about the shapes. ```{r} #| label: figure-tmap1a #| fig-cap: "Our first tmap plot" -library(tmap) |> suppressPackageStartupMessages() -tm_shape(uk_countries) + tm_borders() +tm_shape(uk) + tm_borders() ``` In the example above shown in @figure-tmap1a you can see we've just added a polygon with a border. We can do something similar point data and dots as shown in @figure-tmap1b: @@ -105,7 +104,7 @@ Let's see how those layers get added on with an example (@figure-tmap2): #| label: figure-tmap2 #| fig-cap: "A GGPlot of UK Churches" -tm_shape(uk_countries) + +tm_shape(uk) + tm_borders(alpha=.5, lwd=0.1) + tm_shape(local_authorities) + tm_borders(lwd=0.6) + @@ -128,7 +127,7 @@ Our next step here will be to add all the churches to our map, but there's a pro tm_shape(os_openmap_pow) + tm_dots() + - tm_shape(uk_countries) + + tm_shape(uk) + tm_borders() ``` @@ -140,7 +139,7 @@ You'll recall that in previous chapters, we tried some experiments modifying sca tm_shape(os_openmap_pow) + tm_dots("red", size = .001, alpha = .4) + - tm_shape(uk_countries) + + tm_shape(uk) + tm_borders(alpha=.5, lwd=0.4) ``` @@ -171,7 +170,7 @@ Now let's visualise this data using tmap, which (now that we have that new colum tm_shape(uk_rgn) + tm_borders(alpha=.5, lwd=0.4) + - tm_fill(col = "churches_count", title = "Concentration of churches") + tm_fill(fill = "churches_count", title = "Concentration of churches", tm_scale(breaks = c(0, 30000, 40000, 50000))) ``` Now something strange happened here. We've lost Scotland and Wales! If you look at the legend, you'll see a clue which is that our counts start at 1000 rather than zero, so anything below that threshold in our map simply doesn't exist. This is a problem especially if we are aiming to tell the truth. A quick tweak can ensure that our visualisation @@ -182,7 +181,7 @@ Now something strange happened here. We've lost Scotland and Wales! If you look #| label: figure-tmap6 #| fig-cap: "From dots to choropleth" -tm_shape(uk_rgn) + tm_polygons(fill = "red") +tm_shape(uk_rgn) + tm_fill(fill = "churches_count", tm_scale_intervals(style = "pretty")) ``` We can do the same for our more granular local authorities data: